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遗传算法在甲流SIR模型参数求解中的应用
引用本文:刘嵩,石润,张兰兰.遗传算法在甲流SIR模型参数求解中的应用[J].测绘信息与工程,2012,37(6):6-9.
作者姓名:刘嵩  石润  张兰兰
作者单位:武汉大学遥感信息工程学院,武汉市珞喻路129号,430079
基金项目:国家自然科学基金资助项目
摘    要:论文以全国甲型H1N1流行性感冒(下简称甲流)疫情数据为实例,讨论了采用SIR模型对甲流的传播过程进行模拟时相关参数的求解问题。分别通过优化的遗传算法(Genetic Algorithm,GA)和模拟退火算法(Simula-ted Annealing Algorithm,SA)求得该非线性模型中的重要参数阈值(日治愈率与日传染率的比值),并由该参数阈值计算出各月患病人数。论文比较分析了两种算法在精度和效率上的优劣,发现遗传算法优于模拟退火。同时模拟结果验证了SIR模型适合甲流疫情的分析模拟。

关 键 词:遗传算法  模拟退火算法  甲流SIR模型  非线性模型的参数求解

Application of Genetic Algorithm to SIR Model Parameters
LIU Song,SHI Run,ZHANG Lanlan.Application of Genetic Algorithm to SIR Model Parameters[J].Journal of Geomatics,2012,37(6):6-9.
Authors:LIU Song  SHI Run  ZHANG Lanlan
Institution:(School of Remote Sensing and Information Engineering,Wuhan University,129 Luoyu Road,Wuhan 430079,China)
Abstract:Genetic Algorithms mimic the process of natural revolution with of inheritance,mutation,selection and crossover techniques in order to solve optimization and search problems.This paper focuses on the validity of applying a Genetic Algorithm(GA) to estimate the parameters for a Susceptible-Infectious-Recovered(SIR) model,based on monthly collected data of New Influenza A(H1N1) infections in China on a provincial scale.By estimating an important threshold value in the non-linear SIR model,both Genetic Algorithm and Simulated Annealing(SA) are adopted to calculate the number of infections.The result shows that GA is a more effective method in SIR model calculation in the case of H1N1,considering both precision and efficiency.Our findings indicate that applying GA to the SIR model is appropriate for data fitting,process simulation and trend prediction for the H1N1 pandemic.
Keywords:genetic algorithm  simulated annealing algorithm  SIR model based on H1N1  the estimation of threshold in non-linear model
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